Consistent connectome landscape mining for cross-site brain disease identification using functional MRI.

Many human brain disorders are associated with characteristic alterations in functional connectivity of the brain. A lot of efforts have been devoted to mining disease-related biomarkers for identifying patients with brain disorders from normal controls. However, previous studies show largely inconsistent findings due to variability across numerous study-specific factors such as heterogeneity across different preprocessing pipelines or the use of multi-site data. Also, existing methods usually employ human-engineered features (e.g., graph-theoretical measures) that may be less discriminate for disease identification. To this end, we propose a novel Connectome Landscape Modeling (CLM) method that can mine cross-site consistent connectome landscape and extract data-driven representation of functional connectivity networks for brain disorder identification. Specifically, with functional connectivity networks as input, the proposed CLM model aims to learn a weight matrix for joint cross-site consistent connectome landscape learning, network feature extraction, and disease identification. We impose the row-column overlap norm penalty on the network-based predictor to capture consistent connectome landscape across multiple sites. To capture site-specific patterns, we introduce an ℓ-norm penalty in CLM. We develop an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve the proposed objective function. Experimental results on three real-world fMRI datasets demonstrate the potential use of our CLM in cross-site brain disorder analysis.